Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
CA 02904829 2015-09-18
SYSTEM AND METHOD FOR ESTIMATING AND PROVIDING DISPATCHABLE
OPERATING RESERVE ENERGY CAPACITY THROUGH USE OF ACTIVE LOAD
MANAGEMENT
BACKGROUND OF THE INVENTION
[0001] Field of the Invention
[0002] The present invention relates generally to the field of electric
power supply and
generation systems and, more particularly, to a system and method for
estimating and/or
providing dispatchable operating reserve energy capacity for an electric
utility using active load
management so that the reserve capacity may be made available to the utility
or to the general
power market (e.g., via a national grid).
[0003] Description of Related Art
[0004] Energy demand within a utility's service area varies constantly.
Such variation in
demand can cause undesired fluctuations in line frequency if not timely met.
To meet the
varying demand, a utility must adjust its supply or capacity (e.g., increase
capacity when demand
increases and decrease supply when demand decreases). However, because power
cannot be
economically stored, a utility must regularly either bring new capacity on-
line or take existing
capacity off-line in an effort to meet demand and maintain frequency. Bringing
new capacity
online involves using a utility's reserve power, typically called "operating
reserve." A table
illustrating a utility's typical energy capacity is shown in FIG. 1. As shown,
operating reserve
typically includes three types of power: so-called "regulating reserve,"
"spinning reserve," and
"non-spinning reserve" or "supplemental reserve." The various types of
operating reserve are
discussed in more detail below.
[0005] Normal fluctuations in demand, which do not typically affect line
frequency, are
responded to or accommodated through certain activities, such as by increasing-
or decreasing an
existing generator's output or by adding new generating capacity. Such
accommodation is
generally referred to as "economic dispatch." A type of power referred to as
"contingency
reserve" is additional generating capacity that is available for use as
economic dispatch to meet
changing (increasing) demand. Contingency reserve consists of two of the types
of operating
reserve, namely, spinning reserve and non-spinning reserve. Therefore,
operating reserve
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generally consists of regulating reserve and contingency reserve.
[0006] As shown in FIG. 1, spinning reserve is additional generating
capacity that is already
online (e.g., connected to the power system) and, thus, is immediately
available or is available
within a short period of time after a determined need (e.g., within ten (10)
to fifteen (15)
minutes, as defined by the applicable North American Electric Reliability
Corporation (NERC)
regulation). More particularly, in order for contingency reserve to be
classified as "spinning
reserve," the reserve power capacity must meet the following criteria:
a) be connected to the grid;
b) be measurable and verifiable; and
c) be capable of fully responding to load typically within 10-15 minutes of
being
dispatched by a utility, where the time-to-dispatch requirements of the
spinning
reserve are generally governed by a grid system operator or other regulatory
body,
such as NERC.
[0007] Non-spinning reserve (also called supplemental reserve) is
additional generating
capacity that is not online, but is required to respond within the same time
period as spinning
reserve. Typically, when additional power is needed for use as economic
dispatch, a power
utility will make use of its spinning reserve before using its non-spinning
reserve because (a) the
generation methods used to produce spinning reserve capacity typically tends
to be cheaper than
the methods, such as one-way traditional demand response, used to produce non-
spinning reserve
or (b) the consumer impact to produce non-spinning reserve is generally less
desirable than the
options used to produce spinning reserve due to other considerations, such as
environmental
concerns. For example, spinning reserve may be produced by increasing the
torque of rotors for
turbines that are already connected to the utility's power grid or by using
fuel cells connected to
the utility's power grid; whereas, non-spinning reserve may be produced from
simply turning off
resistive and inductive loads such as heating/cooling systems attached to
consumer locations.
However, making use of either spinning reserve or non-spinning reserve results
in additional
costs to the utility because of the costs of fuel, incentives paid to
consumers for traditional
demand response, maintenance, and so forth.
[0008] If demand changes so abruptly and quantifiably as to cause a
substantial fluctuation in
line frequency within the utility's electric grid, the utility must respond to
and correct for the
change in line frequency. To do so, utilities typically employ an Automatic
Generation Control
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(AGC) process or subsystem to control the utility's regulating reserve. To
determine whether a
substantial change in demand has occurred, each utility monitors its Area
Control Error (ACE).
A utility's ACE is equal to the difference in the scheduled and actual power
flows in the utility
grid's tie lines plus the difference in the actual and scheduled frequency of
the supplied power
multiplied by a constant determined from the utility's frequency bias setting.
Thus, ACE can be
written generally as follows:
ACE = (NIA - NIs) + (-10.131)(FA - Fs), [Equation 1]
where NIA is the sum of actual power flows on all tie lines,
NIs is the sum of scheduled flows on all tie lines,
B1 is the frequency bias setting for the utility,
FA is the actual line frequency, and
Fs is the scheduled line frequency (typically 60 Hz).
[0009] In view of the foregoing ACE equation, the amount of loading
relative to capacity on
the tie lines causes the quantity (NIA - NIs) to be either positive or
negative. When demand is
greater than supply or capacity (i.e., the utility is under-generating or
under-supplying), the
quantity (NIA - NIs) is negative, which typically causes ACE to be negative.
On the other hand,
when demand is less than supply, the quantity (NIA - NIs) is positive (i.e.,
the utility is over-
generating or over-supplying), which typically causes ACE to be positive. The
amount of
demand (e.g., load) or capacity directly affects the quantity (NIA - NIs);
thus, ACE is a measure
of generation capacity relative to load. Typically, a utility attempts to
maintain its ACE very
close zero using AGC processes.
[ONO] If ACE is not maintained close to zero, line frequency can change and
cause problems
for power consuming devices attached to the electric utility's grid. Ideally,
the total amount of
power supplied to the utility tie lines must equal the total amount of power
consumed through
loads (power consuming devices) and transmission line losses at any instant of
time. However,
in actual power system operations, the total mechanical power supplied by the
utility's
generators is seldom exactly equal to the total electric power consumed by the
loads plus the
transmission line losses. When the power supplied and power consumed are not
equal, the
system either accelerates (e.g., if there is too much power in to the
generators) causing the
generators to spin faster and hence to increase the line frequency or
decelerates (e.g., if there is
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not enough power into the generators) causing the line frequency to decrease.
Thus, variation in
line frequency can occur due to excess supply, as well as due to excess
demand.
[0011] To respond to fluctuations in line frequency using AGC, a utility
typically utilizes
"regulating reserve," which is one type of operating reserve as illustrated in
FIG. 1. Regulating
reserve is used as needed to maintain constant line frequency. Therefore,
regulating reserve must
be available almost immediately when needed (e.g., in as little as a few
seconds to less than
about five (5) minutes). Governors are typically incorporated into a utility's
generation system
to respond to minute-by-minute changes in load by increasing or decreasing the
output of
individual generators and, thereby, engaging or disengaging, as applicable,
the utility's
regulating reserve.
[0012] The Federal Energy Reliability Commission (FERC) and NERC have
proposed the
concept of Demand Side Management (DSM) as an additional approach to account
for changes
in demand. DSM is a method in which a power utility carries out actions to
reduce demand
during peak periods. Examples of DSM include encouraging energy conservation,
modifying
prices during peak periods, direct load control, and others.
[0013] Current approaches for using DSM to respond to increases in demand
have included
using one way load switches that interrupt loads, as well as statistics to
approximate the average
amount of projected load removed by DSM. A statistical approach is employed
because of the
utility's inability to measure the actual load removed from the grid as a
result of a DSM load
control event. In addition, current DSM approaches have been limited to use of
a single power
measuring meter among every one hundred (100) or more service points (e.g.,
residences and/or
businesses). Accordingly, current DSM approaches are inadequate because they
rely on
statistical trends and sampling, rather than on empirical data, to make
projections and measure
actual load removal events.
[0014] More recently, FERC and NERC have introduced the concept of flexible
load-shape
programs as a component of DSM. These programs allow customers to make their
preferences
known to the utility concerning timing and reliability of DSM load control
events. However,
DSM approaches utilizing load-shaping programs do not meet all of the criteria
for
implementing regulating reserve or spinning reserve, such as being
dispatchable within 15
minutes or less. Additionally, in order for a generating source to be
considered dispatchable
energy, it must be forecasted twenty-four (24) hours prior to being delivered
to a utility. Current
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DSM approaches do not facilitate accurate forecasting twenty-four (24) hours
in advance due to
their heavy reliance on statistics.
[0015] Therefore, there is a need for utilities to be able to create
operating reserve, especially
regulating and/or spinning reserve, by using accurate forecasting and flexible
load shaping
techniques. There is a further need to involve the consumer in a two-way
approach in which the
consumer can make their energy consumption preferences known and the utility
can make use of
those preferences to respond to increased demand and maintain line frequency
regulation.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 is a table showing the base load power requirements and
operating reserve
available to an electric power utility.
[0017] FIG. 2 is a block diagram illustrating how an active load management
system in
accordance with the present invention provides additional operating (e.g.,
regulating, spinning
and/or non-spinning) reserve to a power utility.
[0018] FIG. 3 is a block diagram of an exemplary IP-based, active load
management system
in accordance with one embodiment of the present invention.
[0019] FIG. 4 is a block diagram illustrating an exemplary active load
director as shown in
the power load management system of FIG. 3.
[0020] FIG. 5 is a block diagram illustrating generation of an exemplary
sampling repository
at the active load director of FIG. 4 or some other location in an electric
utility.
[0021] FIG. 6 is a screen shot of an exemplary web browser interface
through which a
customer may designate his or her device performance and energy saving
preferences for an
environmentally-dependent, power consuming device in accordance with one
embodiment of the
present invention.
[0022] FIG. 7 is a screen shot of an exemplary web browser interface
through which a
customer may designate his or her device performance and energy saving
preferences for an
environmentally-independent, power consuming device in accordance with another
embodiment
of the present invention.
[0023] FIG. 8 is an operational flow diagram illustrating a method for
empirically analyzing
power usage of power consuming devices and populating a repository with data
samples
resulting from such power usage analysis, in accordance with an exemplary
embodiment of the
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present invention.
[0024] FIG. 9 is an operational flow diagram illustrating a method for
projecting energy
usage for a power consuming device in accordance with an exemplary embodiment
of the
present invention.
[0025] FIG. 10 is an operational flow diagram illustrating a method for
estimating power
consumption behavior of a power consuming device in accordance with an
exemplary
embodiment of the present invention.
[0026] FIG. 11 is an operational flow diagram illustrating a method for
projecting energy
savings through power interruption to a power consuming device during a
control event, in
accordance with an exemplary embodiment of the present invention.
[0027] FIG. 12 is a graph that depicts a load profile of a utility during a
projected time
period, showing actual energy usage as well as projected energy usage
determined with and
without a control event, in accordance with an exemplary embodiment of the
present invention.
[0028] FIG. 13 is a block diagram of a system for implementing a virtual
electric utility in
accordance with an exemplary embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0029] Before describing in detail exemplary embodiments that are in
accordance with the
present invention, it should be observed that the embodiments reside primarily
in combinations
of apparatus components and processing steps related to actively monitoring
and managing
power loading at an individual service point (e.g., on an individual
subscriber basis) and
throughout a utility's service area, as well as determining available or
dispatchable operating
reserve power derived from projected power savings resulting from monitoring
and management
of power loading. Accordingly, the apparatus and method components have been
represented
where appropriate by conventional symbols in the drawings, showing only those
specific details
that are pertinent to understanding the embodiments of the present invention
so as not to obscure
the disclosure with details that will be readily apparent to those of ordinary
skill in the art having
the benefit of the description herein.
[0030] In this document, relational terms, such as "first" and "second,"
"top" and "bottom,"
and the like, may be used solely to distinguish one entity or element from
another entity or
element without necessarily requiring or implying any physical or logical
relationship or order
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between such entities or elements. The terms "comprises," "comprising," and
any other
variation thereof are intended to cover a non-exclusive inclusion, such that a
process, method,
article, or apparatus that comprises a list of elements does not include only
those elements, but
may include other elements not expressly listed or inherent to such process,
method, article, or
apparatus. The term "plurality of' as used in connection with any object or
action means two or
more of such object or action. A claim element proceeded by the article "a" or
"an" does not,
without more constraints, preclude the existence of additional identical
elements in the process,
method, article, or apparatus that includes the element.
[0031] Additionally, the term "ZigBee" refers to any wireless communication
protocol
adopted by the Institute of Electronics & Electrical Engineers (IEEE)
according to standard
802.15.4 or any successor standard(s), and the term "Bluetooth" refers to any
short-range
communication protocol implementing IEEE standard 802.15.1 or any successor
standard(s).
The term "High Speed Packet Data Access (HSPA)" refers to any communication
protocol
adopted by the International Telecommunication Union (ITU) or another mobile
telecommunications standards body referring to the evolution of the Global
System for Mobile
Communications (GSM) standard beyond its third generation Universal Mobile
Telecommunications System (UMTS) protocols. The term "Long Term Evolution
(LTE)" refers
to any communication protocol adopted by the ITU or another mobile
telecommunications
standards body referring to the evolution of GSM-based networks to voice,
video and data
standards anticipated to be replacement protocols for HSPA. The term "Code
Division Multiple
Access (CDMA) Evolution Date-Optimized (EVDO) Revision A (CDMA EVDO Rev. A)"
refers to the communication protocol adopted by the ITU under standard number
TIA-856
Rev. A.
10032] The terms "utility," "electric utility," "power utility," and
"electric power utility"
refer to any entity that generates and/or distributes electrical power to its
customers, that
purchases power from a power-generating entity and distributes the purchased
power to its
customers, or that supplies electricity created either actually or virtually
by alternative energy
sources, such as solar power, wind power, load control, or otherwise, to power
generation or
distribution entities through the FERC electrical grid or otherwise. The terms
"energy" and
"power" are used interchangeably herein. The terms "operating reserve,"
"spinning reserve,"
"regulating reserve," "non-spinning reserve," "supplemental reserve," and
"contingency reserve"
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are conventional in the art and their uses and inter-relations are described
in Paragraphs [0005]-
[0008] and [0012] above. The term "environment" refers to general conditions,
such as air
temperature, humidity, barometric pressure, wind speed, rainfall quantity,
water temperature,
etc., at or proximate a service point or associated with a device (e.g., water
temperature of water
in a hot water heater or a swimming pool). The term "device," as used herein,
means a power-
consuming device, and there may generally be two different types of devices
within a service
point, namely, an environmentally-dependent device and an environmentally-
independent
device. An environmentally-dependent device is any power consuming device that
turns on or
off, or modifies its behavior, based on one or more sensors that detect
characteristics, such as
temperature, humidity, pressure, or various other characteristics, of an
environment. An
environmentally-dependent device may directly affect and/or be affected by the
environment in
which it operates. An environmentally-independent device is any power-
consuming device that
turns on or off, or modifies its behavior, without reliance upon inputs from
any environmental
sensors. Generally speaking, an environmentally-independent device does not
directly affect,
and is not typically affected by, the environment in which it operates,
although, as one skilled in
the art will readily recognize and appreciate, operation of an environmentally-
independent device
can indirectly affect, or occasionally be affected by, the environment. For
example, as those
skilled in the art readily understand, a refrigerator or other appliance
generates heat during
operation, thereby causing some heating of the ambient air proximate the
device.
[0033]
It will be appreciated that embodiments or components of the systems described
herein may be comprised of one or more conventional processors and unique
stored program
instructions that control the one or more processors to implement, in
conjunction with certain
non-processor circuits, some, most, or all of the functions for determining an
electric utility's
available or dispatchable operating (e.g., regulating and spinning) reserve
that is derived from
projected power savings resulting from monitoring and management of loads in
one or more
active load management systems as described herein. The non-processor circuits
may include,
but are not limited to, radio receivers, radio transmitters, antennas, modems,
signal drivers, clock
circuits, power source circuits, relays, meters, memory, smart breakers,
current sensors, and user
input devices. As such, these functions may be interpreted as steps of a
method to store and
distribute information and control signals between devices in a power load
management system.
Alternatively, some or all functions could be implemented by a state machine
that has no stored
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program instructions, or in one or more application specific integrated
circuits (ASICs), in which
each function or some combinations of functions are implemented as custom
logic. Of course, a
combination of the foregoing approaches could be used. Thus, methods and means
for these
functions have been described herein. Further, it is expected that one of
ordinary skill in the art,
notwithstanding possibly significant effort and many design choices motivated
by, for example,
available time, current technology, and economic considerations, when guided
by the concepts
and principles disclosed herein, will be readily capable of generating such
software instructions,
programs and integrated circuits (ICs), and appropriately arranging and
functionally integrating
such non-processor circuits, without undue experimentation.
[0034]
Generally, the present invention encompasses a system and method for
estimating
operating reserve (e.g., spinning and/or regulating reserve) for a utility
servicing one or more
service points. In one embodiment, the utility employs an active load
management system
(ALMS) to remotely determine, during at least one period of time, power
consumed by at least
one device located at the one or more service points and receiving power from
the utility to
produce power consumption data. The power consumption data is regularly stored
and updated
in a repository. The ALMS or a control component thereof, such as an active
load director
(ALD), determines an expected, future time period for a control event during
which power is to
be interrupted or reduced to one or more devices. Prior to commencement of the
control event,
the ALMS or its control component: (i) estimates power consumption behavior
expected of the
device(s) during the time period of the control event based at least on the
stored power
consumption data, (ii) determines projected energy savings resulting from the
control event
based at least on the estimated power consumption behavior of device(s), and
determines
operating (e.g., regulating and/or spinning) reserve based on the projected
energy savings. The
determined operating reserve may be made available to the current power
utility or to the power
market through the existing (e.g., Federal Energy Regulatory Commission) power
grid. In one
embodiment, the ALD populates an internal repository (e.g., database, matrix,
or other storage
medium) with measurement data indicating how individual devices within
individual service
points consume power or otherwise behave under normal operation and during
control events.
The power consumption data is updated through regular (e.g., periodic or
otherwise) sampling of
device operating conditions (e.g., current draw, duty cycle, operating
voltage, etc.). When an
ALD is first installed in an ALMS for an, electric utility power grid, there
is little data with which
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to create regulating and spinning reserve forecasts. However, over time, more
and more data
samples are used to improve the quality of the data in the repository. This
repository is used to
project both energy usage and energy savings. These projections can be
aggregated for an entire
service point, a group of service points, or the entire utility.
[0035] In an alternative embodiment, additional data may be used to help
differentiate each
data sample stored in the repository. The additional data is associated with
variability factors,
such as, for example, outside air temperature, day of the week, time of day,
humidity, sunlight,
wind speed, altitude, orientation of windows or doors, barometric pressure,
energy efficiency
rating of the service point, insulation used at the service point, and others.
All of these
variability factors can have an influence on the power consumption of a
device. Some of the
variability factor data may be obtained from public sources, such as local,
state or national
weather services, calendars, and published specifications. Other variability
factor data may be
obtained privately from user input and from sensors, such as humidity,
altitude, temperature
(e.g., a thermostat), and optical or light sensors, installed at or near a
service point (e.g., within or
at a residence or business).
[0036] FIG. 2 is a block diagram illustrating how an ALMS operating in
accordance with the
present invention provides additional operating (e.g., regulating, spinning,
and/or non-spinning)
reserve to a power utility. Without use of an ALMS operating in accordance
with the present
invention, the utility has capacity equal to its base load plus its regulating
reserve, spinning
reserve, and non-spinning reserve as shown on the left side of the figure.
However, with use of
an ALMS operating in accordance with the present invention, the utility has
additional operating
reserve, which may be preferably used as regulating, spinning and/or non-
spinning reserve (as
illustrated in FIG. 2), by drawing power selectively from service points
through the interruption
or reduction of power to devices, such as air conditioners, furnaces, hot
water heaters, pool
pumps, washers, dryers, boilers, and/or any other inductive or resistive
loads, at the service
points.
[0037] The present invention can be more readily understood with reference
to FIGs. 3-12, in
which like reference numerals designate like items. FIG. 3 depicts an
exemplary IP-based active
load management system (ALMS) 10 that may be utilized by an electric utility,
which may be a
conventional power-generating utility or a virtual utility, in accordance with
the present
invention. The below description of the ALMS 10 is limited to specific
disclosure relating to
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embodiments of the present invention. A more general and detailed description
of the ALMS 10
is provided in U.S. Patent Application Publication No. US 2009/0062970 A1,
which was
published on March 5, 2009. U.S. Patent Application Publication No. US
2009/0062970 Al
provides details with respect to the exemplary operational implementation and
execution of
control events to interrupt or reduce power to devices located at service
points, such as
residences and businesses. The use of an ALMS 10 to implement a virtual
utility is described in
detail in U.S. Patent Application Publication No. US 2009/0063228 A1, which
was published on
March 5, 2009.
100381
The ALMS 10 monitors and manages power distribution via an active load
director
(ALD) 100 connected between one or more utility control centers (UCCs) 200
(one shown) and
one or more active load clients (ALCs) 300 (one shown) installed at one or
more service points
20 (one exemplary residential service point shown). The ALD 100 may
communicate with the
utility control center 200 and each active load client 300 either directly or
through a network 80
using the Internet Protocol (IP) or any other (IP or Ethernet) connection-
based protocols. For
example, the ALD 100 may communicate using RF systems operating via one or
more base
stations 90 (one shown) using one or more wireless communication protocols,
such as GSM,
ANSI C12.22, Enhanced Data GSM Environment (EDGE), HSPA, LTE, Time Division
Multiple
Access (TDMA), or CDMA data standards, including CDMA 2000, CDMA Revision A,
CDMA
Revision B, and CDMA EVDO Rev. A. Alternatively, or additionally, the ALD 100
may
communicate via a digital subscriber line (DSL) capable connection, cable
television based IP
capable connection, or any combination thereof. In the exemplary embodiment
shown in FIG. 3,
the ALD 100 communicates with one or more active load clients 300 using a
combination of
traditional IP-based communication (e.g., over a trunked line) to a base
station 90 and a wireless
channel implementing the HSPA or EVDO protocol from the base station 90 to the
active load
client 300. The distance between the base station 90 and the service point 20
or the active load
client 300 is typically referred to as the "last mile" even though the
distance may not actually be
a mile. The ALD 100 may be implemented in various ways, including, but not
limited to, as an
individual server, as a blade within a server, in a distributed computing
environment, or in other
combinations of hardware and software. In the following disclosure, the ALD
100 will be
described as embodied in an individual server to facilitate an understanding
of the present
invention. Thus, the server embodiment of the ALD 100 described below
corresponds generally
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to the description of the ALD 100 in US Patent Application Publication Nos. US
2009/0062970
Al and US 2009/0063228 A1.
100391 Each active load client 300 is preferably accessible through a
specified address (e.g.,
IP address) and controls and monitors the state of individual smart breaker
modules or intelligent
appliances 60 installed at the service point 20 (e.g., in the business or
residence) to which the
active load client 300 is associated (e.g., connected or supporting). Each
active load client 300 is
preferably associated with a single residential or commercial customer. In one
embodiment, the
active load client 300 communicates with a residential load center 400 that
contains smart
breaker modules, which are able to switch from an "ON" (active) state to an
"OFF" (inactive)
state, and vice versa, responsive to signaling from the active load client
300. Smart breaker
modules may include, for example, smart breaker panels manufactured by
Schneider Electric SA
under the trademark "Square D" or Eaton Corporation under the trademark
"Cutler-Hammer" for
installation during new construction. For retro-fitting existing buildings,
smart breakers having
means for individual identification and control may be used. Typically, each
smart breaker
controls a single appliance (e.g., a washer/dryer 30, a hot water heater 40,
an HVAC unit 50, or a
pool pump 70). In an alternative embodiment, IP addressable relays or device
controllers that
operate in a manner similar to a "smart breaker" may be used in place of smart
breakers, but
would be installed coincident with the load under control and would measure
the startup power,
steady state power, power quality, duty cycle and energy load profile of the
individual appliance
60, HVAC unit 40, pool pump 70, hot water heater 40, or any other controllable
load as
determined by the utility or end customer.
100401 Additionally, the active load client 300 may control individual
smart appliances
directly (e.g., without communicating with the residential load center 400)
via one or more of a
variety of known communication protocols (e.g., IP, Broadband over PowerLine
(BPL) in its
various forms, including through specifications promulgated or being developed
by the
HOMEPLUG Powerline Alliance and the Institute of Electrical and Electronic
Engineers (IEEE),
Ethernet, Bluetooth, ZigBee, Wi-Fi (IEEE 802.11 protocols), HSPA, EVDO, etc.).
Typically, a
smart appliance 60 includes a power control module (not shown) having
communication
abilities. The power control module is installed in-line with the power supply
to the appliance,
between the actual appliance and the power source (e.g., the power control
module is plugged
into a power outlet at the home or business and the power cord for the
appliance is plugged into
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the power control module). Thus, when the power control module receives a
command to turn
off the appliance 60, it disconnects the actual power supplying the appliance
60. Alternatively,
the smart appliance 60 may include a power control module integrated directly
into the
appliance, which may receive commands and control the operation of the
appliance directly (e.g.,
a smart thermostat may perform such functions as raising or lowering the set
temperature,
switching an HVAC unit on or off, or switching a fan on or off).
100411 The active load client 300 may further be coupled to one or more
variability factor
sensors 94. Such sensors 94 may be used to monitor a variety of variability
factors affecting
operation of the devices, such as inside and/or outside temperature, inside
and/or outside
humidity, time of day, pollen count, amount of rainfall, wind speed, and other
factors or
parameters.
100421 Referring now to FIG. 4, the ALD 100 may serve as the primary
interface to
customers, as well as to service personnel, and operates as the system
controller sending control
messages to, and collecting data from, installed active load clients 300 as
described in detail
below and in U.S. Patent Application Publication No. US 2009/0062970 Al. In
the exemplary
embodiment depicted in FIG. 4, the ALD 100 is implemented as an individual
server and
includes a utility control center (UCC) security interface 102, a UCC command
processor 104, a
master event manager 106, an ALC manager 108, an ALC security interface 110,
an ALC
interface 112, a web browser interface 114, a customer sign-up application
116, customer
personal settings 138, a customer reports application 118, a power savings
application 120, an
ALC diagnostic manager 122, an ALD database 124, a service dispatch manager
126, a trouble
ticket generator 128, a call center manager 130, a carbon savings application
132, a utility power
and carbon (P&C) database 134, a read meter application 136, a security device
manager 140, a
device controller 144, and one or more processors 160 (one shown). The
operational details of
several of the elements of the ALD 100 are described below with respect to
their use in
connection with the present invention. The operational details of the
remaining elements of the
ALD 100 may be found in U.S. Patent Application Publication Nos. US
2009/0062970 Al and
US 2009/0063228 A1, wherein the ALD 100 is also described in the context of an
individual
server embodiment.
100431 In one embodiment, a sampling repository is used to facilitate the
determination of
dispatchable operating reserve power or energy (e.g., spinning and/or
regulating reserve) for a
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utility. An exemplary sampling repository 500 is shown in block diagram form
in FIG. 5. As
illustrated in FIG. 5, the sampling repository 500 is a means for storing
device monitoring data
and other data that collectively details how devices (e.g., a hot water heater
40 as shown in FIG.
5) have behaved under specific conditions. The repository 500 may be in
various forms,
including a matrix, a database, etc. In one embodiment, the sampling
repository 500 is
implemented in the ALD database 124 of the ALD 100. Alternatively, the
sampling repository
500 may reside elsewhere within the ALD 100 or be external to the ALD 100. The
sampling
repository 500 contains all power consumption data for devices located at a
device or service
point 20 or within a utility. Power consumption data may include, but is not
limited to: current
reading, energy/power used or consumed, energy/power saved, drift or drift
rate, power time,
user settings for maximum environmental variances, time periods (e.g., hours
of the day, days of
the week, and calendar days). Taken collectively, this data is used to show
how devices behaved
during normal operation as well as during control events in which power is
temporarily
interrupted or reduced to one or more devices. The data may be obtained via
passive sampling
(e.g., regular monitoring of devices at a particular service point 20 by the
active load client 300
associated with the service point 20) and/or active sampling (e.g., direct
polling of the devices
for the data by the active load client 300 or the ALD 100). As discussed
below, the sampling
repository 500 is used by the ALD 100 or other components of the ALMS 10 to
estimate or
project power consumption behavior of the devices and to determine projected
power/energy
savings resulting from a control event. The projected power savings may be
determined using
the power savings application 120 based upon the power consumption data in the
repository 500.
[0044]
FIG. 6 is an exemplary screen shot displayed to a user (e.g., customer) during
execution of a customer personal settings application 138. The illustrated
screen shot shows a
screen being used to set the customer preferences for an environmentally-
dependent device, such
as an HVAC unit 50, a humidifier, or a pool heater. The illustrated screen
shot may be provided
to the customer in one embodiment via an Internet-accessible web portal 98
(referred to herein as
the "customer dashboard") when such portal is accessed by the customer via a
computer, smart
phone, or other comparable device. As shown in FIG. 3, the customer dashboard
98 may be
connected to the ALD 100 via an Internet service provider for the service
point 20 or may be
implemented as a customer Internet application 92 when Internet service is
supplied through the
active load client 300 as described in U.S. Patent Application Publication No.
US 2009/0063228
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AL The customer dashboard 98 effectively provides the customer with access
into the ALD
100. The ALD's web browser interface 114 accepts inputs from the customer
dashboard 98 and
outputs information to the customer dashboard 98 for display to the customer.
The customer
dashboard 98 may be accessed from the service point 20 or remotely from any
Internet-
accessible device, preferably through use of a user name and password. Thus,
the customer
dashboard 98 is preferably a secure, web-based interface used by customers to
specify
preferences associated with devices controlled by the ALD 100 and located at
the customer's
service point 20, as well as to provide information requested by the customer
personal settings
application 138 or the customer sign-up application 116 in connection with
controlled devices
and/or service point conditions or parameters. Customer preferences may
include, for example,
control event preferences (e.g., times, durations, etc.), bill management
preferences (e.g., goal or
target for maximum monthly billing cost), maximum and minimum boundary
settings for
environmental characteristics, and others.
[0045] FIG. 7 is another exemplary screen shot displayed to a customer via
the customer
dashboard 98 during execution of a different portion of the customer personal
settings
application 138. FIG. 7 shows how customer preferences could be set for an
environmentally-
independent device, such as a hot water heater 40, a pool pump 70, or a
sprinkler system water
pump (which may also be an environmentally-dependent device if it includes,
for example, a
rainfall sensor). Using the web browser interface 114, customers interact with
the ALD 100 and
specify customer personal settings 138 that are recorded by the ALD 100 and
stored in the ALD
database 124 or other repository 500. The personal settings 138 may specify
time periods during
which load control events are permitted, time periods during which load
control events are
prohibited, maximum allowable variances for an operating environment at a
particular service
point 20 (e.g., maximum and minimum temperature and/or humidity), normal
operating
conditions of devices at different times of day, and other personal
preferences related to
operation of devices under the control of the ALD 100 through the active load
client 300 at the
service point 20.
[0046] As alluded to above, the present invention optionally tracks and
takes into account the
"drift" of an environmentally-dependent device. Drift occurs when the
environmental
characteristic(s) (e.g., temperature) monitored by an environmentally-
dependent device begins to
deviate (e.g., heat up or cool down) from a set point that is to be maintained
by the
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environmentally-dependent device. Such deviation or drift may occur both
normally and during
control events. Thus, drift is the time it takes for the monitored
environmental characteristic to
move from a set point to an upper or lower comfort boundary when power, or at
least substantial
power, is not being consumed by the device. In other words, drift is a rate of
change of the
monitored environmental characteristic from a set point without use of
significant power (e.g.,
without powering an HVAC unit compressor, but while continuing to power an
associated digital
thermostat and HVAC unit control system). One of ordinary skill in the art
will readily
appreciate that devices, such as HVAC units 50, which control one or more
environmental
characteristics at a service point 20, are also influenced or affected by the
environment at the
service point 20 because their activation or deactivation is based on one or
more sensed
environmental characteristics at the service point 20. For example, an HVAC
unit 50 in cooling
mode that attempts to maintain an inside temperature of 77 F activates when
the inside
temperature is some temperature greater than 77 F and, therefore, is
influenced or affected by
the environment in which the HVAC unit 50 operates.
[0047] The inverse of drift is "power time," which is the time it takes for
the sensed
environmental characteristic to move from a comfort boundary to a set point
when significant or
substantial power is being supplied to the environmentally-dependent device.
In other words,
"power time" is a rate of change of the monitored environmental characteristic
from a comfort
boundary to a set point with significant use of power. Alternatively, "drift"
may be considered
the time required for the monitored environmental characteristic to move to an
unacceptable
level after power is generally turned off to an environmentally-dependent
device. By contrast,
"power time" is the time required for the monitored environmental
characteristic to move from
an unacceptable level to a target level after power has been generally
supplied or re-supplied to
the environmentally-dependent device.
[0048] The power consumption data for an environmentally-dependent device,
which may be
gathered actively or passively as described above, may be used to empirically
determine the drift
and power time (rate of change, temperature slope, or other dynamic equation
(f{x})) that
defines an environmental characteristic's variation at a service point 20, or
at least within the
operating area of the environmentally-dependent device, so as to permit the
determination of a
uniquely derived "fingerprint" or power usage/consumption pattern or behavior
for the service
point 20 or the environmentally-dependent device.
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[0049] Customers define the upper and lower boundaries of comfort by
inputting customer
preferences 138 through the web browser interface 114, with the set point
optionally being in the
middle of those boundaries. During normal operation, an environmentally-
dependent device will
attempt to keep the applicable environmental characteristic or characteristics
near the device's
set point or set points. However, all devices, whether environmentally-
dependent or
environmentally-independent, have a duty cycle that specifies when the device
is in operation
because many devices are not continuously in operation. For an environmentally-
dependent
device, the duty cycle ends when the environmental characteristic(s) being
controlled reaches the
set point (or within a given tolerance or variance of the set point). After
the set point has been
reached, the environmentally-dependent device is generally turned off and the
environmental
characteristic is allowed to "drift" (e.g., upward or downward) toward a
comfort boundary. Once
the environmental characteristic (e.g., temperature) reaches the boundary, the
environmentally-
dependent device is generally activated or powered on again until the
environmental
characteristic reaches the set point, which ends the duty cycle and the power
time.
[0050] Drift may also occur during a control event. A control event is an
action that
temporarily reduces, terminates, or otherwise interrupts the supply of power
to a device. During
a control event, the environmental characteristic (e.g., temperature)
monitored and/or controlled
by an environmentally-dependent device will drift toward a comfort boundary
(e.g., upper or
lower) until the environmental characteristic reaches that boundary. Once the
environmental
characteristic reaches the boundary, the ALMS 10 generally returns or
increases power to the
device to enable the environmental characteristic to reach the set point
again.
[0051] For example, an HVAC unit 50 may have a set point of 72 F and
minimum and
maximum comfort boundary temperatures of 68 F and 76 F, respectively. On a
cold day, a
control event may interrupt power to the HVAC unit 50 causing the monitored
temperature
within the service point 20 to move toward the minimum comfort boundary
temperature. Once
the monitored temperature inside the service point 20 reaches the minimum
comfort boundary
temperature, the control event would end, and power would be restored or
increased to the
HVAC unit 50, thus causing the monitored temperature to rise toward the set
point. A similar,
but opposite effect, may take place on a warm day. In this example, "drift" is
the rate of change
with respect to the time it takes the HVAC unit 50 to move from the set point
to either the upper
or lower comfort bounds. Analogously, "power time" is the rate of change with
respect to the
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time required for the HVAC unit 50 to move the monitored temperature from the
upper or lower
comfort bounds to the set point. In one embodiment of the present invention,
drift and power
time are calculated and recorded for each environmentally-dependent or
environmentally-
independent device or for each service point 20.
[0052] In another embodiment, drift and other measurement data available
from the ALD
database 124 are used to create a power consumption behavior or pattern for
each
environmentally-dependent or environmentally-independent device or for each
service point 20.
The other measurement data may include vacancy times, sleep times, times in
which control
events are permitted, and/or other variability factors.
[0053] The environment within an energy-efficient structure will have a
tendency to exhibit a
lower rate of drift. Therefore, environmentally-dependent devices operating
within such
structures may be subject to control events for longer periods of time because
the amount of time
taken for the monitored environmental characteristic to reach a comfort
boundary due to drift
after being set to a set point is longer than for less efficient structures.
[0054] In another embodiment, the ALD 100 may identify service points 20
that have an
optimum drift for power savings. The power savings application 120 calculates
drift for each
service point 20 and/or for each environmentally-dependent device at the
service point 20, and
saves the drift information in the ALD database 124 as part of power
consumption data for the
device and/or the service point 20. Thus, power saved as a result of drift
during a control event
increases overall power saved by the environmentally-dependent device at the
service point 20.
[0055] FIG. 8 illustrates an exemplary operational flow diagram 800
providing steps
executed by the ALD 100 to empirically analyze power usage of devices and
populate a
repository 500 with data samples resulting from such power usage analysis, in
accordance with
one embodiment of the present invention. The steps in FIG. 8 may be considered
to implement a
passive sampling algorithm. The steps of FIG. 8 are preferably implemented as
a set of
computer instructions (software) stored in memory (not shown) of the ALD 100
and executed by
one or more processors 160 of the ALD 100.
100561 According to the logic flow, the active load client 300 polls
devices within the service
point 20, such as a washer/dryer 30, hot water heater 40, HVAC unit 50, smart
appliance 60,
pool pump 70, or other devices within the service point 20, and obtains
current readings. Upon
receiving the current reading data from the active load client 300, the ALC
interface 112 sends
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the data to the ALC manager 108. The ALC manager 108 stores the data to the
sampling
repository 500, which may be implemented in the ALD database 124 using the
operational flow
illustrated in FIG. 8.
[0057] The following information may be provided as parameters to the
operational flow of
FIG. 8: an identification (ID) of the device, temperature mode (either
"heating" or "cooling"),
duty cycle, current temperature read by the device, and previous temperature
read by the device.
Each temperature reading includes a device ID, a set point (which is only
useful for
environmentally-dependent devices), and variability factor measurement data
(as described
previously).
[0058] Initially, the ALD 100 determines (802) whether the device used any,
or at least any
appreciable amount of, energy. If not, then the logic flow ends. Otherwise,
the ALD 100
determines (804) the time duration of the data sample, the time duration when
the device was on,
and the time duration when the device was off based on the data sample. Next,
the ALD 100
determines (806) whether the received data comes from an environmentally-
dependent device or
an environmentally-independent (e.g., binary state) device. If the received
data comes from an
environmentally-dependent device, then the ALD 100 determines (808) the energy
used per
minute for the device, and determines (810) whether the device is drifting or
powering. The
ALD 100 determines that the device is drifting if the environmental
characteristic monitored by
the device is changing in a manner opposite the mode of the device (e.g., the
room temperature is
rising when the device is set in cooling mode or the room temperature is
decreasing when the
device is set in heating mode). Otherwise, the device is not drifting.
[0059] If the device is drifting, then the ALD 100 determines (814) the
drift rate (e.g.,
degrees per minute). On the other hand, if the device is not drifting, then
the ALD 100
determines (812) the power time rate. Once either the drift rate or the power
time rate has been
calculated, the ALD 100 determines (880) whether there is already a record in
the sampling
repository 500 for the device being measured under the present operating
conditions of the
device (e.g., set point and other variability factors (e.g., outside
temperature)). If there is no
existing record, then the ALD 100 creates (882) a new record using, for
example, the device's
ID, time of record, current set point, current outside temperature, energy
used per minute, power
time rate, and drift rate (assuming that either a power time rate or a drift
rate has been
determined). However, if there is an existing record, then the ALD 100 updates
(884) the
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existing record by averaging the new data (including energy usage, drift rate,
and power time
rate) with the existing data and storing the result in the repository 500.
[0060] If the ALD 100 determines (806) that the received data comes from an
environmentally-independent device, then the ALD 100 determines (842) the
energy used per
minute for the device and further determines (844) the energy saved per minute
for the device.
The ALD 100 then searches the repository 500 (e.g., ALD database (124)) to
determine (890)
whether there is already a record for the device for the applicable time
period. If there is no
existing record, then the ALD 100 creates (892) a new record using the
device's ID, time of
record, current time block, energy used per minute, and energy saved per
minute. However, if
there is an existing record, then the ALD 100 updates (894) the existing
record by averaging the
new data (including energy usage and energy savings) for the time block with
the existing data
for the time block and stores the result in the repository 500. For
environmentally-independent
devices, energy usage and energy savings are saved with respect to a block or
period of time.
[0061] FIG. 9 illustrates an exemplary operational flow diagram 900
providing steps
executed by the ALD 100 to project or estimate the energy usage expected of a
device during a
future time period in a given environment setting, in accordance with one
embodiment of the
present invention. The steps of FIG. 9 are preferably implemented as a set of
computer
instructions (software) stored in memory (not shown) of the ALD 100 and
executed by one or
more processors 160 of the ALD 100. In accordance with one embodiment, the
operational flow
of FIG. 9 may be executed by the power savings application 120 of the ALD 100
when a utility
operator, or other operator of the ALD 100, wants to project the energy usage
for a device over a
specified time period in the future, such as during a period of time in which
a control event is to
occur.
[0062] The following information may be provided as parameters to the
operational flow of
FIG. 9: the device ID, the start time of the future time period, the end time
of the future time
period, the manage mode of the device, and, for an environmentally-independent
device, a binary
control factor. The manage mode is either "control" or "normal" to indicate
whether the device
is being measured during a control event or during normal operation,
respectively. The binary
control factor is preferably utilized for environmentally-independent devices
and represents the
duty cycle of the device. For example, if a water heater 40 runs at 20% duty
cycle, the binary
control factor is 0.2.
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[0063] Initially, the ALD 100 (e.g., power savings application 120)
determines (902) a future
time period based on the start and stop times. The future time period may be
set by the utility
implementing the load control procedure of the present invention or a second
utility that has
requested delivery of operating reserve power from the utility implementing
the load control
procedure of the present invention. After the time period at issue is known,
the power savings
application 120 begins the procedure for projecting or estimating the amount
of power that can
be saved as the result of execution of a control event during the future time
period. Accordingly,
the power savings application 120 analyzes the devices to be controlled during
the control event.
Thus, the power savings application 120 determines (904) whether the devices
include both
environmentally-dependent and environmentally-independent (e.g., binary state)
devices. For
each environmentally-dependent device, the power savings application 120
determines (920)
whether the device is in environment controlling (e.g., heating or cooling)
mode. Next, the
power savings application 120 retrieves (922) the anticipated set points for
the device during the
future time period of the control event and obtains (924) information
regarding the outside
environmental characteristic(s) (e.g., the outside temperatures) expected
during the control event
time period. The power savings application 120 then makes projections (926)
about the device's
expected power consumption behavior during the future time period. In one
embodiment, the
projection determination of block 926 is implemented using a best match
algorithm, as described
in detail below with respect to FIG. 10, to find stored repository records
that best match the
behavior of the device for each combination of set points, outside
environmental characteristics
(e.g., temperatures), and time periods, as measured and stored using the logic
flow of FIG. 8.
The power consumption behavior of the device is used to determine the amount
of energy that
would be expected to be used by the device if the control event did not occur
and, thus, the
amount of energy estimated or expected to be saved per unit time during the
control event. The
power savings application 120 multiplies (928) the saved power per unit time
by the time
duration of the future control event to determine the total amount of energy
projected to be used
by the device in the absence of the control event. The power savings
application returns (980)
the total projected amount of energy used by the device in the absence of the
proposed control
event.
[0064] However, if the power savings application 120 determines (904) that
the proposed
control event is to affect an environmentally-independent device, then the
power savings
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application 120 determines (960) whether the device is currently scheduled to
be on or off during
the proposed time period of the control event. Next, the power savings
application 120 creates,
obtains, or otherwise determines (962) a list of time blocks for the specified
control event time
period. The power savings application 120 then makes projections (964) about
the device's
power consumption behavior during the future, control event time period. In
one embodiment,
the projection determination of block 964 is implemented using a best match
algorithm, as
described in detail below with respect to FIG. 10, to find stored repository
records that best
match the behavior of the device for each combination of set points, outside
environmental
characteristics (e.g., temperatures), and time periods, as measured and stored
using the logic flow
of FIG. 8. The power consumption behavior of the device is used to determine
the amount of
energy that would be expected to be used by the device if the control event
did not occur and,
thus, the amount of energy estimated or expected to be saved per unit time
during the control
event. Next, the power savings application 120 multiplies (968) the saved
power per unit time
by the time duration of the future control event to determine the total amount
of energy projected
to be used in the absence of the control event. If the projected energy
savings is based on power
consumption during a previous control event (970), then the power savings
application 120
multiplies (972) the total amount of energy times the binary control factor to
determine the
amount of energy projected to be used by the device in the absence of the
control event. The
power savings application returns (980) the total projected amount of energy
used by the device
in the absence of the proposed control event.
100651 One or ordinary skill in the art will readily recognize and
appreciate that the
operational flow of FIG. 9 may be used for each controlled device at a service
point, for the
controlled devices at multiple service points, or for all the controlled
devices at all the service
points supplied or supported by a utility. The total projected energy usage by
the devices may be
aggregated across a single service point, for all service points within a
group, and/or for all
groups served by the utility.
100661 FIG. 10 illustrates an exemplary operational flow diagram 1000
providing steps
executed by the ALD 100 for estimating power consumption behavior of a device
in accordance
with an exemplary embodiment of the present invention. The algorithm or
operational flow
illustrated in FIG. 10 provides one embodiment for implementing steps 926 and
964 of FIG. 9.
The operational flow of FIG. 10 determines which record or records in the
sampling repository
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500 provides the closest match to a given environment or operational setting
for use in projecting
device energy usage/savings during a time period of a future control event, in
accordance with
one embodiment of the present invention. The steps of FIG. 10 are preferably
implemented as a
set of computer instructions (software) stored in memory (not shown) of the
ALD 100 and
executed by one or more processors 160 of the ALD 100. The operational flow of
FIG. 10 may
be initiated by the ALD 100 when trying to identify or determine the sampling
repository record
or records that best match the power consumption behavior of a device in a
specific setting.
[0067]
In one embodiment, the operational flow of FIG. 10 is called during execution
of the
operational flow of FIG. 9 as noted above. When so called, the operational
flow of FIG. 9
provides the operational flow of FIG. 10 with parameters that indicate the
type of records to be
searched. These parameters include, but are not limited to: a device ID, a
duty mode (either on
or off), a time period (e.g., corresponding to the time period of the proposed
future control
event), a set point delta, a delta or variance related to one or more
environmental characteristics
(e.g., outside temperature), and a time block delta. Duty mode signifies the
duty cycle of the
device. If the duty mode is TRUE or ON, significant power is being consumed.
If the duty
mode is FALSE or OFF, significant power is not being consumed (i.e., power is
being saved).
Duty cycle exists for switch-controlled, binary state, or environmentally-
independent devices
which go ON and OFF irrespective of the influence or affect of environment.
For HVAC
devices 50, duty mode is always ON. Set point delta is the amount a set point
may be varied
during a search in order to find a matching repository record.
Outside
temperature/environmental characteristic delta is the number of temperature
degrees or other
change in environmental characteristics over which data relating to the
outside temperature or
other environmental characteristics may be varied during a search in order to
find a matching
repository record. Time block delta is the amount of time a time block may be
varied during a
search in order to find a matching repository record.
[0068]
Initially, the ALD 100 determines (1002) whether the requested repository
search
relates to an environmentally-dependent device or an environmentally-
independent device. If the
search relates to an environmentally-dependent device, then the ALD 100
attempts to find (1004)
power consumption records in the sampling repository 500 that match the device
ID, duty mode,
environmental characteristic (e.g., temperature) set point, and associated
outside environmental
characteristic data. Power consumption records include power consumption data,
such as power
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consumed, current drawn, duty cycle, operating voltage, operating impedance,
time period of
use, set points, ambient and outside temperatures during use (as applicable),
and/or various other
energy use data. If a record exists that matches all the power consumption
search criteria, such
record would be considered the record that most closely matches the given
environment setting.
If no exact match is found (1010), then the ALD 100 begins looking for records
that slightly
differ from the given environment setting. In one embodiment, the ALD 100
incrementally
increases or decreases (1012) the environment-related search criteria (e.g.,
temperature set point
and/or outside/ambient temperature) using the set point delta and the outside
temperature/environmental characteristic delta as a guide to look for relevant
records. Such
incremental/iterative modification of the search criteria continues until
either relevant records are
found or some applicable limit (e.g., as indicated by the set point delta
and/or other parameter
deltas) is reached.
[0069] If the ALD 100 determines (1002) that the search relates to an
environmentally-
independent device, then the ALD 100 attempts to find (1040) power consumption
records in the
sampling repository 500 that match the device ID, duty mode, and time of
operation
(corresponding to the expected, future time of the control event). If a record
is not found that
matches all the search criteria (1070), then the ALD 100 modifies its search
to look for records
that slightly differ from the given environment setting. In one embodiment,
the ALD 100
modifies its search by incrementally increasing or decreasing (1072) the time
of operation for a
given duty mode. The iterative searching continues until either relevant
records are found or
some applicable limit (e.g., as indicated by the time block delta or other
parameter deltas) is
reached. Any records that were found as a result of the search are provided
(1060) to the
requesting program (e.g., the operational flow of FIG. 9). The result of the
operational flow of
FIG. 10 is a set of one or more power consumption records from the sampling
repository 500 that
are the closest match to the given environment or proposed control event
setting.
[0070] FIG. 1 1 illustrates an exemplary operational flow diagram 1100
providing steps
executed by the ALD 100 to project energy savings through power interruption
or reduction to a
device during a control event, in accordance with one embodiment of the
present invention. The
steps of FIG. 11 are preferably implemented as a set of computer instructions
(software) stored in
memory (not shown) of the ALD 100 and executed by one or more processors 160
of the ALD
100. As with the operational flow of FIG. 9, the operational flow of FIG. 11
may be executed by
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the power savings application 120 when an operator of the utility or of the
ALD 100 wants to
project the energy savings for a device over a specified time period during
operation of a control
event.
[0071] The following information may be provided as parameters to the
operational flow of
FIG. 11: a device ID, a start time of the control event, an end time of the
control event, and a
binary control factor, as described above in connection with FIG. 9.
Initially, the ALD 100 (e.g.,
power savings application 120) projects (1102) the energy usage/power
consumption for the
device during normal operation within the expected time period of the control
event using, for
example, the operational flow of FIG. 9. Next, the power savings application
120 projects
(1104) the power consumption for the device during the control event using,
for example, the
operational flow of FIG. 9. For example, depending on the duty cycle, set
points, drift or drift
rate, power time, and other parameters for the device, the device may be
projected to be on and
consuming power for some amount of time during the time period of the control
event. Thus,
both the expected amount of power consumed during normal operation (i.e., in
the absence of
any control event) and the expected amount of power consumed during the
control event are
determined to accurately assess any possible power savings as a result of the
control event. After
the two projected power consumption values have been determined, the power
savings
application 120 calculates (1106) the difference between the two values, which
is the projected
power consumption for the device during the control event time period. Because
the projected
power consumption will not be realized during the control event, such power
consumption
corresponds directly to an amount of energy saved during the control event.
The power savings
application 120 returns (1108) the projected energy savings value. One of
ordinary skill in the
art will readily recognize and appreciate that the power savings application
120 may aggregate
the projected power savings for all controlled devices at a service point 20,
for all controlled
devices at service points within a group, or for controlled devices within all
service point groups
served by the utility to obtain an aggregate amount of power savings as a
result of a control
event.
[0072] Another context in which the ALMS 10 may be utilized is in
conjunction with other
renewable energy sources. A number of renewable energy sources, such as wind
power and
solar power, are variable in nature. That is, such energy sources do not
generate power at a
constant rate. For example, wind increases or decreases from moment to moment.
Wind
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turbines can generate a large amount of power due to large winds or can stop
generating
completely due to lack of any wind. Solar panels may be able to generate a
great deal of power
on very sunny days, a little power on cloudy days, and virtually no power at
night.
100731 As a result, power utilities that make use of renewable energy must
compensate for
the under-generation or over-generation of power from those sources. When
renewable energy
sources are under-generating, the ALMS 10 may utilize the processes disclosed
above to provide
additional operating reserve to compensate for the lack of power generation by
the renewable
energy source and for the effects resulting therefrom, including output
frequency instability. For
example, a utility utilizing wind or solar energy sources may further
incorporate the ALMS 10
into the utility distribution system to provide regulating reserve during time
periods of under-
generation.
[00741 FIG. 12 is a graph that depicts the "load profile" of a utility over
a predetermined
time period, showing actual energy usage as well as projected energy usage
determined with and
without a control event in accordance with an exemplary embodiment of the
present invention.
The load profile graph depicts the following:
a. Baseline power consumption 1202. This is the total possible load of, or
power
consumed by, all controlled devices over a specified period of time.
b. Projected interruptible load usage 1204 (i.e., projected load or energy
usage with
a control event) for all controlled devices at all service points (or at
selected
service points) served by the utility in the absence of a control event. The
projected interruptible load usage may be determined in one embodiment through
execution of the operational flow of FIG. 9. The projected interruptible load
available 1204 indicates the load for all controlled devices if they are
controlled
100% of the time using customer preferences.
c. Projected interruptible load available 1206 (i.e., projected energy used
when no
control events are used) for all controlled devices at all service points (or
at
selected service points) served by the utility during a control event. The
projected
interruptible load usage may be determined in one embodiment through execution
of the operational flow of FIG. 11.
d. Actual interruptible load usage 1208 for all controlled devices at all
service points
(or at selected service points) served by the utility. The actual
interruptible load
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usage 1208 is the power that is currently being used by all controlled
devices.
This type of load profile graph may be generated for all controlled devices at
a service point 20,
for controlled devices at all service points within a group, or for controlled
devices at all groups
served by the utility.
[0075] In the load profile graph of FIG. 12, the capacity under contract is
shown as a straight
double line at the top of the graph and indicates the baseline power
consumption 1202. The
baseline power consumption 1202 represents the total amount of power that the
utility is
obligated to provide. The actual interruptible load usage 1208 is the actual
energy usage of all
devices controlled by the utility. The projected interruptible load usage 1204
at the bottom of the
load profile graph is the projected energy used when control events are used,
and the projected
interruptible load available 1206 is the projected energy usage when control
events are not used.
The difference between the projected interruptible load usage 1204 and the
projected
interruptible load available 1206 is the capacity that may be used for
operating reserve, including
regulating reserve, spinning reserve, and non-spinning reserve.
[0076] Normally, when a utility observes energy demand that is near its
peak capacity, it will
attempt to initiate control events for customers who voluntarily participate
in power saving
programs (i.e., flexible load-shape programs, as described earlier).
Typically, these control
events will provide sufficient capacity to prevent the utility from using non-
spinning reserve.
However, there are situations in which a sufficient number of customers may
have manually
decided to opt out of power saving programs and, as a result, the utility
would be unable to
recover enough energy to meet its spinning reserve needs from its remaining
customers who
voluntarily participate in the program. Such a situation could happen, for
instance, on a very hot
day when many people are home, such as on a holiday or a day over the weekend.
In such a
case, the utility would still be in danger of using non-spinning reserve or
even running out of
reserve capacity altogether. In such a situation, the utility would be in a
"critical control" mode.
In critical control mode, the utility may override all customer preferences,
including both those
who voluntarily participate in power saving programs and those who do not.
During periods of
critical control, the utility may utilize the ALD 100 to adjust settings of
environmentally-
dependent devices to settings outside of normal comfort preferences (but not
life-threatening).
Invoking critical control enables a utility to return power demand to
acceptable levels.
[0077] Use of the ALMS 10 may help a utility mitigate the likelihood of
critical control
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situations. For example, whenever a customer overrides or opts out of a
control event, the
ALMS 10, using the techniques disclosed herein, finds additional customers who
may be the
target of a voluntary control event. Analogously, when controlled devices that
are participating
in a control event are required to exit the control event due to customer
preferences (e.g., the
amount of time that the customer's devices may participate in a control
event), the ALD 100 may
release such devices from the control event and replace them with other
voluntarily controlled
devices. The replacement devices would then preferably supply, through
deferment, at least the
same amount of reserve power as was being sourced by the devices that were
released from the
control event. Thus, the system 10 of the present invention increases the
likelihood that a utility
will be able to spread control events to other customers before invoking
critical control.
[0078] In a further embodiment, the entire ALMS 10 described in FIG. 3 may
also be
implemented in a proprietary network that is IP-based, real-time, temperature-
derived, verifiable,
interactive, two-way, and responsive to Automatic Generation Control (AGC)
commands to
produce operating reserve power through implementation of control events.
[0079] In an additional embodiment of the present invention, the sampling
data stored in the
repository 500 using the operational flow of FIG. 5 could also include other
factors (called
"variability factors") related to power consumption, such as day of the week,
humidity, amount
of sunshine, or number of people in the household. This additional data would
allow the
projected energy usage and projected energy savings to be more accurate based
on these
additional factors. To make use of this data, the ALD 100 may obtain the
additional data from
sources within and/or external to the ALMS 10, such as weather databases, live
weather feeds
from sources such as National Weather Reporting stations, outdoor sensors 94,
or any weather
related input device commercially available on a real time or predictive
basis, calendars, and
voluntary customer feedback. Some of the variability factor measurements are
available from
public sources, while others are available via private sources.
[0080] In another alternative embodiment of the present invention,
transmission line loss
may be included in the projected energy savings determination of FIG. 11. As
those of ordinary
skill in the art will recognize and appreciate, the amount of power supplied
by a utility to source
a device remote from the utility equals the amount of power required by the
device plus the
amount of power lost in the transmission lines between the utility's power
generation plant and
the location of the device. Thus, the projected energy savings resulting from
a control event may
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be determined by determining an amount of power expected to be consumed by the
controlled
device or devices at a service point, at multiple service points or throughout
the entire service
area of the utility during the time period of the control event absent
occurrence of the control
event to produce first energy savings, determining an amount of power that is
not expected to be
dissipated in transmission lines as a result of not delivering power to the
controlled device or
devices during the control event to produce second energy savings, and summing
the first energy
savings and the second energy savings.
[0081] In a further embodiment of the present invention, the operating
reserve (e.g., spinning
reserve or regulating reserve) determined by a utility using the techniques
disclosed above can be
sold to a requesting utility 1306, as illustrated in FIG. 13, which is
essentially a replication of
FIG. 9 of U.S. Patent Application Publication No. US 2009/0063228 Al. As
explained in U.S.
Patent Application Publication No. US 2009/0063228 A1, the saved power may
then be
distributed to the requesting utility 1306 after commencement of the control
event (e.g., during
and/or after completion of the control event) conducted by the selling
utility. The selling utility
may be a virtual utility 1302 or a serving utility 1304 as illustrated in FIG.
13 and described in
detail in U.S. Patent Application Publication No. US 2009/0063228 Al.
Alternatively, a third
party may serve as a managing entity to manage operation of the ALMS 10 and
the resultant
distribution of operating reserve to a requesting utility 1306 subsequent to
commencement of a
control event.
[0082] In yet another embodiment, the ALD 100 for a utility may determine
projected energy
savings for each service point 20 served by the utility in accordance with the
operational flow of
FIG. 11 and aggregate the projected energy savings across all service points
served by the utility
to obtain the total projected energy savings from which operating reserve may
be determined as
described above.
[0083] In a further embodiment, instead of or in addition to using the
operational flow of
FIG. 10 in an attempt to find a best match data point in the repository 500
for use in estimating
power consumption behavior of a device when the time period of the control
event does not
correspond to a time period in the repository 500, the ALD 100 may determine
whether the
repository 500 includes power consumption data for the device during time
periods before and
after the expected time period of the control event and, if so, interpolate a
value corresponding to
an amount of power expected to be consumed by the device during the time
period of the control
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event based on the power consumption data for the device during the time
periods before and
after the expected time period of the control event.
[0084] In yet another embodiment, a requesting utility may utilize a method
for acquiring
operating reserve power from a sourcing utility. According to this embodiment,
the requesting
utility requests operating reserve power from the sourcing utility
sufficiently in advance of a
transfer time at which the operating reserve power will be needed so as to
facilitate measurable
and verifiable load-controlled generation of the operating reserve power. The
load-controlled
generation of the operating reserve power results from a determination of
operating reserve as
detailed above with respect to FIGs. 7-12. The requesting utility receives an
acknowledgment
from the sourcing utility indicating that the sourcing utility will supply the
operating reserve
power at the transfer time. Then, at the transfer time and for a time period
thereafter, the
requesting utility receives at least some of the operating reserve power from
the sourcing utility.
[0085] In a further embodiment, the operating reserve determination
techniques may be
utilized by a virtual utility 1302 as disclosed in U.S. Patent Application
Publication No. US
2009/0063228 A 1 . For example, the virtual utility 1302 may be operable to at
least offer energy
to one or more requesting utilities 1306 for use as operating reserve for the
requesting utilities
1306. In such a case, the virtual utility 1302 may include, among other
things, a repository 500
and a processor 160 (e.g., within an ALD 100). In this embodiment, the
processor 160 is
operable to remotely determine, during at least one period of time, power
consumed by at least
one device to produce power consumption data. The processor 160 is further
operable to store
the power consumption data in the repository 500 and, at the appropriate time,
determine an
expected, future time period for a control event during which power is to be
reduced to the
device or devices. The processor 160 is also operable to estimate, prior to
commencement of the
control event, power consumption behavior expected of the device or devices
during the time
period of the control event based at least on the stored power consumption
data. The processor
160 is further operable to determine, prior to commencement of the control
event, projected
energy savings resulting from the control event based at least on the
estimated power
consumption behavior of the device or devices. Still further, the processor
160 is operable to
determine, prior to commencement of the control event, operating reserve based
on the projected
energy savings. After determination of the operating reserve, the processor
160 is operable to
communicate an offer to supply the operating reserve to a requesting utility
1306 or utilities.
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[0086] As described above, the present invention encompasses a system and
method for
determining operating reserve capacity using an ALD or comparable device,
software, or
combination thereof so that the operating reserve capacity may be made
available to the power
utility that generated the operating reserve through load control or to the
power market generally
(e.g., via the FERC grid). When a utility requires power beyond its native
load, the utility must
make use of its operating reserve or acquire the additional power via the FERC
grid from other
utilities. As discussed above, one type of operating reserve is spinning
reserve. Spinning reserve
is additional generating capacity that is already connected to the power
system and, thus, is
almost immediately available. In accordance with one embodiment of the present
invention, the
ALD makes spinning reserve available to a utility. Thus, through use of the
ALD, a utility
(power generating utility or a virtual utility) can determine or project
spinning reserve or other
operating reserve that is available through interruptible power savings at
service points. The
spinning reserve is measurable and verifiable, and can be projected for a
number of days in
advance, and such projections can be sold to other utilities on the open
market.
[0087] As disclosed above, the ALD 100 may be considered to implement a
type of flexible
load-shape program. However, in contrast to conventional load control
programs, the load-shape
program implemented by the ALD 100 projects an amount of operating reserve
resulting from
selective control of devices (loads) based on known, real-time customer
preferences. In addition,
due to its communication and control mechanisms, the ALD 100 can project power
savings, as
well as operating reserve (e.g., regulating, spinning and/or non-spinning
reserve) that is active,
real-time, verifiable, and measurable so as to comply with protocols and
treaties established for
the determination of carbon credits and offsets, as well as renewable energy
credits. The
information acquired by the ALD 100 is not simply samples of customer
preferences and data,
but actual power consumption information.
[0088] In the foregoing specification, the present invention has been
described with reference
to specific embodiments. However, one of ordinary skill in the art will
appreciate that various
modifications and changes may be made without departing from the spirit and
scope of the
present invention as set forth in the appended exemplary claims. For example,
the passive
sampling algorithm of FIG. 8, the projected energy usage algorithm of FIG. 9,
the best sampling
match algorithm of FIG. 10, and the projected energy savings algorithm of FIG.
11 may be
performed by one or more equivalent means. Accordingly, the specification and
drawings are to
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be regarded in an illustrative rather than a restrictive sense, and all such
modifications are
intended to be included within the scope of the present invention.
100891
Benefits, other advantages, and solutions to problems have been described
above with
regard to specific embodiments of the present invention. However, the
benefits, advantages,
solutions to problems, and any element(s) that may cause or result in such
benefits, advantages,
or solutions to become more pronounced are not to be construed as a critical,
required, or
essential feature or element of any or all the claims. The invention is
defined solely by the
appended claims including any amendments made during the pendency of this
application and all
equivalents of those claims as issued.
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